To verify the proposed approach, an experiment was performed on a real robot dataset, which is available for public access [1]}. As shown in Fig. REF , there are two grid maps with different resolutions, which were produced by the robot exploring different parts of the same environment. To achieve grid maps, the scale-invariant feature transform (SIFT) features [2]} were extracted and matched for two grid maps to be merged. Then, the random sample consensus (RANSAC) algorithm [3]} was applied to confirm two geometrically consistent feature matches, which can be utilized to estimate the initial transformation for the scaling registration algorithm. As shown in Fig. REF , the initial transformation is inaccurate and the merging map is coarse. Subsequently, the edge points can be extracted from these two grid maps. According to our previous work [4]}, the problem of merging grid maps with different resolutions can be reasonably viewed as the scaling registration of the partially overlapping point sets. Therefore, the sTrICP algorithm can be applied to the registration of extracted edge point sets and obtain the accurate scaling transformation. Finally, these two grid maps can be merged into a single global map, which is more fine than the initial merged one.
